In order to guarantee the confidentiality of the local training model from malicious attackers, some study concentrates on employing differential privacy for users' data privacy protection. In [1]}, the authors proposed a hybrid approach to tackle the problem of inference attacks and provide privacy-preserving federated learning using differential privacy and secure multi-party computation (SMPC). This approach aims to address the FL challenges, such as inference attacks and lack of accuracy, as well as recede the enlargement of noise injection when the number of users rises in diverse use cases and applications. Further, the study in [2]} proposed the NbAFL framework to evade data leakage using a differential privacy technique by adding noise before FL model aggregation. This study focuses on solving the information leakage in the distributed stochastic gradient descent (SGD) based FL and develops a theoretical convergence bound for the trained FL model's loss function.
